rowfun

Arguments

f

function to apply with prototype [r1, ..., rN] = f(x1, ..., xN).

t

table or timeseries object in input

Name, Value

Name: 'InputVariables', Value: variable names or variable indices: the function will be apply only on this specified variables.

Name: 'GroupingVariables', Value: variable names or variable indices: variables values are used to create unique groups.

Name: 'OutputVariableNames', Value: strings: variable names for output table corresponding to the wanted output arguments of the function f. If omitted, the value is generated using 'NumOutputs' ("Var1", ...).

Name: 'NumOutputs', Value: real: number of wanted output arguments of the function f. If omitted, the default value is 1 or size of 'OutputVariableNames'

r

table or timeseries object (same object as t).

Description

The rowfun function returns a table or timeseries (same object as t) where each row contains the result of the function applied to each row of t.

r = rowfun(f, t) applies the function f to each row of t.

To apply the function only on certain table variables, InputVariables must be specified: r = rowfun(f, t, "InputVariables", ["Var1", ...,]). It is also possible to use the syntax v = rowfun(f, t(:, ["Var1", ...,])).

r = rowfun(f, t, "GroupingVariables", ["Var1", ...,]) allows to specify the variables values are used to create unique groups. The function f will be applied to each group of rows of t.

The function f can have multiple output arguments. r = rowfun(..., NumOutputs, val) allows to specify the number of output arguments. To give a name of output variable, r = rowfun(..., OutputVariableNames, ["VarOutput1", ...]) syntax must be used.

Examples

r = rowfun(f, t)

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

r = rowfun(f, t)

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts)

r = rowfun(f, t, "InputVariables", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
x3 = floor(rand(5,1)*5)+1;
t = table(x1, x2, x3, "VariableNames", ["x1", "x2", "x3"])

r = rowfun(f, t, "InputVariables", ["x1", "x3"])

// Possible with extraction: t(:, ["var1", ..., "varN"])
r = rowfun(f, t(:, ["x1", "x3"]))

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, x3, "VariableNames", ["timestamp", "x1", "x2", "x3"])

r = rowfun(f, ts, "InputVariables", ["x1", "x3"])
r = rowfun(f, ts(:, ["x1", "x3"]))

v = rowfun(f, t, "GroupingVariables", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x = ["a"; "b"; "b"; "c"; "a"];
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
x3 = floor(rand(5,1)*5)+1;
t = table(x, x1, x2, "VariableNames", ["x", "x1", "x2"])

r = rowfun(f, t, "GroupingVariables", "x")

t.x3 = x3;
r = rowfun(f, t, "GroupingVariables", "x", "InputVariables", ["x1", "x3"])

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts, "GroupingVariables", "timestamp")

v = rowfun(..., "OutputVariableNames", ["Var1", ...])

function z=f(x, y)
    z = x + y;
endfunction

rand("seed", 0)
x = ["a"; "b"; "b"; "c"; "a"];
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x, x1, x2, "VariableNames", ["x", "x1", "x2"])

r = rowfun(f, t, "GroupingVariables", "x", "OutputVariableNames", "result")

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, ts, "GroupingVariables", "timestamp", "OutputVariableNames", "result")

function [z1, z2, z3]=f(x, y)
    z1 = x + y;
    z2 = x - y;
    z3 = x.^2 + y.^2;
endfunction

rand("seed", 0)
x1 = floor(rand(5,1)*5)-1.5;
x2 = -floor(rand(5,1)*5)+0.5;
t = table(x1, x2, "VariableNames", ["x1", "x2"])

r = rowfun(f, t, "OutputVariableNames", ["a", "b", "c"])
r = rowfun(f, t, "NumOutputs", 3)

// With timeseries
timestamp = hours([1 2 2 3 3])';
ts = timeseries(timestamp, x1, x2, "VariableNames", ["timestamp", "x1", "x2"])

r = rowfun(f, t, "OutputVariableNames", ["a", "b", "c"])
r = rowfun(f, t, "NumOutputs", 3)

See also

• table — create a table from variables
• timeseries — create a timeseries - table with time as index
• varfun — apply a function to each column of the table/timeseries
• groupcounts — returns the number of elements for each group
• groupsummary — create groups in table or timeseries and apply functions to variables within groups
• pivot — create a pivoted table providing a summary of data.